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TL;DR
ThorstenMeyerAI.com’s Nov. 10 essay examines how knowledge and information work grew into a major part of the U.S. economy, drawing on estimates by Fritz Machlup and Marc Porat. It argues that generative AI is aimed at many of the tasks associated with this fourth sector, but says current evidence does not establish broad job losses caused by AI.
ThorstenMeyerAI.com published an essay on November 10 examining how information and knowledge work grew into a major part of the U.S. economy—and why that history matters as generative AI targets office tasks. The essay draws on historical estimates by economist Fritz Machlup and information-economy researcher Marc Porat, while cautioning that exposure to AI does not by itself show that a job has been eliminated.
Machlup’s 1962 study, The Production and Distribution of Knowledge in the United States, estimated that the cost of producing and distributing knowledge in the United States reached $136.4 billion in 1958, or “almost 29 per cent of adjusted GNP.” The essay explains that this was a measure of spending, not a direct calculation of knowledge’s value. Education made up the largest share of the listed knowledge-industry spending.
Porat’s 1977 report, The Information Economy, classified workers by the tasks they performed rather than the industry or workplace employing them. In his estimates, information workers rose from 5.8% of the U.S. workforce in 1860 to 46.4% in 1970. The essay says information work had become the largest of the four groups—agriculture, industry, services and information—by the mid-1950s, according to Porat’s account.
The essay connects that longer shift to current AI concerns: generative systems can perform or assist with some reading, writing, calculation and information-handling tasks. It cites figures attributed to the International Labour Organization’s 2025 data showing managers, professionals and technicians represented 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. Those occupational shares describe the composition of employment; they do not measure AI-driven job losses.
Infographic 01 · The number to remember
Fritz Machlup's first count of the knowledge industries, by branch.
US spending on knowledge by branch, 1958 · share of $136.4 bn
Machlup measured what knowledge cost (spending, the way national accounts count), not what it was worth. “Almost 29 per cent of adjusted GNP” is his figure for 1958. The branch values are as tabulated by Godin (2008); the labour shares are Machlup’s estimate as summarised by his publisher.
Sources: F. Machlup, The Production and Distribution of Knowledge in the United States (Princeton University Press, 1962), as tabulated by B. Godin, “The Knowledge Economy: Fritz Machlup's Construction of a Synthetic Concept” (2008); Princeton University Press, book description.
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AI Reaches the Information Economy
The development matters because AI is being applied to tasks central to knowledge work, not only to physical production. If these tools change how organizations handle writing, analysis, administration or communication, the effects could reach a large share of workers in higher-income economies. That makes the question relevant to employers, workers and policymakers well beyond the technology sector.
But the article distinguishes potential exposure from demonstrated displacement. It reports that overall U.S. employment data show no clear AI footprint so far, while citing research suggesting younger workers in the most AI-exposed jobs are lagging behind peers. The supplied material does not provide the underlying study details or establish that AI caused that gap. The evidence therefore points to a labor-market issue worth tracking, not a settled account of what AI has already done.
The historical figures also show why measurement matters. Machlup counted the cost of knowledge production, while Porat classified occupations by their information-related duties. Neither measure directly tells readers how much value AI will create, how many jobs it may change, or who will benefit. Those outcomes remain separate questions from the growth of the information economy itself.
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From Knowledge to Information Work
The essay places the present AI debate in a longer economic history. In 1959, management writer Peter Drucker described the rise of “knowledge work” in The Landmarks of Tomorrow. Drucker’s idea was that more people were earning a living through what they knew rather than mainly through physical labor, a shift that could require different ways of organizing work than factory-era methods.
Machlup tried to quantify that development three years later by adding up spending across education, research and development, communications media, information machines and information services. His estimate that these activities represented almost 29% of adjusted U.S. GNP in 1958 is an historical accounting result, not a current estimate of the sector’s size.
Porat later used a different approach, sorting workers according to what they did during the workday. Under that method, an office worker employed by a factory could count as an information worker. The essay calls this broad area the quaternary, or information, sector. The distinction helps explain why AI discussions often focus on office work even when the tools are developed by technology companies.
What AI Exposure Shows So Far
It is not yet clear how many jobs AI has eliminated or materially changed. The source essay says overall U.S. employment data show no clear AI footprint, but does not identify the specific data series, time period or method behind that assessment. It also refers to younger workers in highly exposed roles falling behind peers without supplying the underlying figures or enough detail to determine cause.
The article does not name particular AI products, compare their performance with human work, or give a forecast for job losses. Nor does the historical growth of knowledge work prove that AI will reproduce earlier employment shifts. Exposure is not equivalent to replacement: outcomes may depend on how organizations adopt the tools, which tasks they automate, and whether new or changed work offsets reductions elsewhere.
Evidence to Watch in Employment
The immediate next step is to track labor-market evidence over time, including hiring, wages and employment among workers in occupations with high exposure to generative AI. Clearer assessments will require explicit time periods and comparison groups, as well as research that separates AI’s effects from other economic changes.
The source describes the essay as the final installment of its first series arc and points to a subsequent discussion of what economic statistics measure—and what they leave out. That measurement question will matter to future AI claims: historical sector estimates, task exposure and actual job outcomes are different kinds of evidence and should not be treated as interchangeable.
Key Questions
What is the news in this article?
ThorstenMeyerAI.com published an essay on November 10, 2026, connecting the historical growth of knowledge and information work with generative AI’s focus on office tasks.
What did Machlup estimate?
Machlup estimated that U.S. spending on producing and distributing knowledge was $136.4 billion in 1958, or almost 29% of adjusted GNP. The figure measures costs, not knowledge’s direct economic value.
Does the article show that AI is causing widespread job losses?
No. It says overall U.S. employment data show no clear AI footprint so far. It discusses possible exposure and a reported concern about younger workers, but does not establish widespread AI-caused job losses.
What does “fourth sector” mean here?
It refers to the information or quaternary sector, covering work centered on producing, handling or sharing information. Porat classified workers by their tasks, not simply by the industry where they worked.
Source: ThorstenMeyerAI.com
